Thesis (Ph. D.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains xiii, 180 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 138-151).

published_or_final_version / Microbiology / Master / Master of Philosophy

Systems biology studies the complex interactions between components of biological systems. One major goal of systems biology is to reconstruct the network of interactions between genes in response to normal and perturbed conditions. In order to accomplish this goal, large-scale data are needed. Accordingly, diverse powerful and high-throughput methods must be developed for this purpose. We have developed novel high-throughput technologies focusing on cellular phenotype profiling and now provide additional genome-scale analysis of gene and protein function. Few high-throughput methods can perform large-scale and high-throughput cellular phenotype profiling. However, analyzing gene expression patterns and protein behaviors in their cellular context will provide insights into important aspects of gene function. To complement current genomic approaches, we developed two technologies, the spotted cell microarray (cell chip) and the yeast spheroplast microarray, which allow high-throughput and highly-parallel cellular phenotype profiling including cell morphology and protein localization. These methods are based on printing collections of cells, combined with automated high-throughput microscopy, allowing systematic cellular phenotypic characterization. We used spotted cell microarrays to identify 15 new genes involved in the response of yeast to mating pheromone, 80 proteins associated with shmoo-tip 'localizome' upon pheromone stimulation and 5 genes involved in regulating the localization pattern of a group II intron encoded reverse transcriptase, LtrA, in Escherichia coli. Furthermore, in addition to morphology assays, yeast spheroplast microarrays were built for high-throughput immunofluorescence microscopy, allowing large-scale protein and RNA localization studies. In order to identify additional cell cycle genes, especially those difficult to identify in loss-of-function studies, we performed a genome-scale screen to identify yeast genes with overexpression-induced defects in cell cycle progression. After measuring the fraction of cells in G1 and G2/M phases of the cell cycle via high-throughput flow cytometry for each of ~5,800 ORFs and performing the validation and secondary assays, we observed that overexpression of 108 genes leads to reproducible and significant delay in the G1 or G2/M phase. Of 108 genes, 82 are newly implicated in the cell cycle and are likely to affect cell cycle progression via a gain-of-function mechanism. The G2/M category consists of 87 genes that showed dramatic enrichment in the regulation of mitotic cell cycle and related biological processes. YPR015C and SHE1 in the G2/M category were further characterized for their roles in cell cycle progression. We found that the G2/M delay caused by the overexpression of YPR015C and SHE1 likely results from the malfunction of spindle and chromosome segregation, which was supported by the observations of highly elevated population of large-budded cells in the pre-M phase, super-sensitivity to nocodazole, and high chromosome loss rates in these two overexpression strains. While the genes in the G2/M category were strongly enriched for cell cycle associated functions, no pathway was significantly enriched in the G1 category that is composed of 21 genes. However, the strongest enrichment for the G1 category consists of the genes involved in negative regulation of transcription. For instance, the overexpression of SKO1, a transcription repressor, resulted in strong cell cycle delay at G1 phase. Moreover, we found that the overexpression of SKO1 results in cell morphology changes that resembles mating yeast cells (shmoos) and activates the mating pheromone response pathway, thus explaining the G1 cell cycle arrest phenotype of SKO1 ORF strains.

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Since the early 1990s, the concept of substantial equivalence has been a guiding principle of the Canadian Food Inspection Agency and Health Canada's regulatory approach toward products of plant biotechnology destined for the food and livestock feed markets. To assess substantial equivalence in terms of chemical composition, genetically modified (GM) plants are compared to conventional counterparts at the level of macro- and micro-nutrients, allergens and toxicants. Such targeted comparative analyses are limited in their scope and their capacity to detect unintended changes in chemical composition. There is a need to develop more effective testing protocols to improve the substantial equivalence assessment of GM crops. The objective of this thesis was to explore high-density oligoarrays as tools to assess substantial equivalence of Roundup Ready(TM) soybean. Three conventional and two GM soybean varieties were selected according to the similarity of their performance in field trials. Total RNA was extracted from first trifoliate leaves harvested from soybean plants grown in a controlled environment until the V2 stage. To annotate the 37 776 soybean probesets present on the multi-organism Soybean Affymetrix GeneChip(TM), consensus sequences were aligned with TIGR Soybean Gene Index tentative consensus sequences using BLASTN. After redefining the chip description file to exclude non-soybean probesets, the effects of three different normalization methods (Robust Multichip Average (RMA), Microarray Analysis Suite (MAS 5.0) and Model-Based Expression Index) were compared and Significance Analysis of Microarrays (SAM for R-Bioconductor) was applied to detect differential gene expression between conventional and GM soybean varieties. Eleven candidate genes were selected for further studies.

<p>In this thesis, we have firstly cloned and expressed the domains that harbours the putative catalytic GC domain in these receptor molecules and demonstrate that these molecules can convert GTP to cGMP in vitro. Secondly, we show that exogenous application of both Phytosulfokine and Brassinosteroid increase changes of intracellular cGMP levels in Arabidopsis mesophyll protoplast demonstrating that these molecules have GC activity in vivo and therefore provide a link as second messenger between the hormones and down-stream responses. In order to elucidate a relationship between the kinase and GC domains of the PSK receptor, we have used the AtPSKR1 receptor as a model and show that it has Serine/Threonine kinase activity using the Ser/Thr peptide 1 as a substrate. In addition, we show that the receptor`s ability to phosphorylate a substrate is affected by the product (cGMP) of its co-domain (GC) and that the receptor autophosphorylates on serine residues and this step was also observed to be affected by cGMP. When Arabidopsis plants are treated with a cell permeable analogue of cGMP, we note that this can affect changes in the phosphoproteome in Arabidopsis and conclude therefore that the cGMP plays a role in kinase-dependent downstream signalling. The obtained results suggest that the receptor molecules investigated here belong to a novel class of GCs that contains both a cytosolic kinase and GC domains, and thus have a domain organisation that is not dissimilar to that of atrial natriuretic peptide receptors NPR1 and NPR2. The findings also strongly suggest that cGMP has a role as a second messenger in both Brassinosteroid and Phytosulfokine signalling. We speculate that other proteins with similar domain organisations may also have dual catalytic activities and that a significant number of GCs, both in plants and animals, remain to be discovered and characterised.</p>

Microarray technology allows researchers to monitor the mRNA transcription levels of thousands of genes in parallel which opens the door for more advanced cancer research. This thesis focuses on a case study of papillary thyroid carcinoma data. Fourteen publicly available Affymetrix microarray data sets were used where seven samples were collected from normal thyroid tissue and the remaining seven were collected from papillary thyroid carcinoma tissue. The present study compared the results obtained from three different normalization processes: MAS5.0, RMA and GCRMA in detecting differentially expressed genes under two conditions. Internal consistencies within the methods as well as the results across three methods were compared. Statistical packages 82.5.1 and Bioconductor 2.08 are used to perform the data analysis. Each step of normalization with MAS5.0 and RMA is described. Statistical package Limma is used to fit a linear model. Finally an empirical Bayes method is used to detect the significantly differentially expressed genes. First, considering all genes a comparison is made among the three normalization methods where RMA and GCRMA showed the maximum agreement in detecting differentially expressed genes. Then using unspecified filtering process a set of genes was selected and the whole process was replicated where the top fifty differentially expressed genes did not show any overlap with each other. / Department of Mathematical Sciences

Microarray technology like that of recombinant DNA and the polymerase chain reaction is a foundational component of modern biotechnology. There are many aspects in the manufacture of a DNA chip and each serves as a contributing variable to the overall performance of the device. Commercial production of microarrays commenced before a basic understanding of the physical and chemical processes that govern the many variables was attained and the role of each on the performance of microarrays was understood. The consequence of this has been non-optimal performance of this technology and variable results from microarray analyses. Recent studies have shown that part of the variability in the use of commercially available microarrays relates to insufficient control of the surface chemistry within a microarray spot and between spots. Although the ideal characteristics of a microarray surface have been identified, existing surfaces and the ongoing proliferation of alternative ones do not address these sufficiently in order to reduce the sources of error arising from the surface of a microarray. Hence, the application of physico-chemical techniques in the development of microarrays surfaces can assist in improving the performance of these devices. / Thesis (PhDApSc(MineralsandMaterials))--University of South Australia, 2006.

Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, the issue is addressed as a consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, a new concept of performance-based consistency is proposed in this thesis.An interesting finding in our previous experiments is that by using a proper set of informative genes, we significantly improved the consistency characteristic of microarray data. Therefore, how to select genes in terms of consistency modelling becomes an interesting topic. Many previously published gene selection methods perform well in the cancer diagnosis domain, but questions are raised because of the irreproducibility of experimental results. Motivated by this, two new gene selection methods based on the proposed performance-based consistency concept, GAGSc (Genetic Algorithm Gene Selection method in terms of consistency) and LOOLSc (Leave-one-out Least-Square bound method with consistency measurement) were developed in this study with the purpose of identifying a set of informative genes for achieving replicable results of microarray data analysis.The proposed consistency concept was investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy.As an implementation of the proposed performance-based consistency, GAGSc and LOOLSc are capable of providing a small set of informative genes. Comparing with those traditional gene selection methods without using consistency measurement, GAGSc and LOOLSc can provide more accurate classification results. More importantly, GAGSc and LOOLSc have demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments.

Thesis (Ph.D.)--Tufts University, 2004. / Adviser: David R. Walt. Submitted to the Dept. of Chemistry. Includes bibliographical references. Access restricted to members of the Tufts University community. Also available via the World Wide Web;